---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: roberta-finetuned-CPV_Spanish
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta-finetuned-CPV_Spanish

This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on a dataset derived from Spanish Public Procurement documents from 2019. The whole fine-tuning process is available in the following [Kaggle notebook](https://www.kaggle.com/code/marianavasloro/fine-tuned-roberta-for-spanish-cpv-codes).
It achieves the following results on the evaluation set:
- Loss: 0.0152
- F1: 0.9462
- Roc Auc: 0.9698
- Accuracy: 0.9297
- Coverage Error: 3.6573
- Label Ranking Average Precision Score: 0.9451

## Intended uses & limitations

This model only predicts the first two digits of the CPV codes.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step   | Validation Loss | F1     | Roc Auc | Accuracy | Coverage Error | Label Ranking Average Precision Score |
|:-------------:|:-----:|:------:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
| 0.0287        | 1.0   | 20385  | 0.0270          | 0.8235 | 0.8815  | 0.7695   | 10.4603        | 0.8167                                |
| 0.0172        | 2.0   | 40770  | 0.0199          | 0.8773 | 0.9210  | 0.8306   | 7.5943         | 0.8768                                |
| 0.01          | 3.0   | 61155  | 0.0168          | 0.9028 | 0.9364  | 0.8639   | 6.2111         | 0.9045                                |
| 0.0062        | 4.0   | 81540  | 0.0152          | 0.9207 | 0.9520  | 0.8871   | 5.1353         | 0.9213                                |
| 0.0037        | 5.0   | 101925 | 0.0151          | 0.9300 | 0.9569  | 0.9026   | 4.7350         | 0.9295                                |
| 0.0021        | 6.0   | 122310 | 0.0147          | 0.9365 | 0.9625  | 0.9123   | 4.2946         | 0.9355                                |
| 0.0013        | 7.0   | 142695 | 0.0148          | 0.9396 | 0.9659  | 0.9184   | 3.9912         | 0.9387                                |
| 0.001         | 8.0   | 163080 | 0.0150          | 0.9426 | 0.9680  | 0.9243   | 3.8065         | 0.9422                                |
| 0.0006        | 9.0   | 183465 | 0.0152          | 0.9445 | 0.9693  | 0.9274   | 3.7064         | 0.9438                                |
| 0.0003        | 10.0  | 203850 | 0.0152          | 0.9462 | 0.9698  | 0.9297   | 3.6573         | 0.9451                                |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6